Network failure diagnosis method based on selective hidden Naive Bayesian classifier

Abstract

The invention discloses a network failure diagnosis method based on a selective hidden Naive Bayesian classifier, comprising: (1), obtaining history data from a network history database, wherein the history data comprise a symptom variable set and a failure class variable set; (2), constructing a selective hidden Naive Bayesian classifier prediction model, determining corresponding most related symptom variable set according to every symptom variable in the symptom variable set; (3), automatically learning classifier parameters by the selective hidden Naive Bayesian classifier through training the history data; (4), in failure diagnosis, estimating the test data by using the selective hidden Naive Bayesian classifier so as to obtain corresponding final failure diagnosis result. Through executing the network failure diagnosis method of the invention, the problems in the existing network failure diagnosis that the operation complexity is high and the network diagnosis result is great in deviation are effectively solved; the network diagnosis accuracy is greatly improved; the operation complexity is further reduced, and better learning capability and fault-tolerant character are kept at the same time.

Description

technical field [0001] The present invention relates to the technical field of communication networks, and more specifically, to a network fault diagnosis method based on a selective hidden naive Bayesian classifier. Background technique [0002] Modern networks are characterized by large scale and high network topology complexity. Failure of one part of the network can cause a series of symptoms. If the fault is not diagnosed in time, the system function, reliable operation, and safe production of the entire network will be affected, and even the network will be paralyzed. Therefore, fault diagnosis is particularly important for the network. [0003] Early fault diagnosis relies entirely on expert knowledge, but it is difficult to maintain the reliability and stability of network operation. Therefore, in large and complex networks, intelligent diagnosis is widely used, and some methods have also been proposed, such as Bayesian network and artificial intelligence. [0004...

AI Technical Summary

Problems solved by technology

[0007] In view of the above defects or improvement needs of the prior art, the present invention provides a network fault diagnosis method based on a selective hidden naive Bayesian classifie

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